Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations471
Missing cells4
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory184.1 KiB
Average record size in memory400.4 B

Variable types

Numeric14
Categorical4
Text1

Alerts

DEM is highly overall correlated with ID and 7 other fieldsHigh correlation
FIPS is highly overall correlated with STATE and 1 other fieldsHigh correlation
FRG is highly overall correlated with NLCDHigh correlation
ID is highly overall correlated with DEM and 6 other fieldsHigh correlation
KFactor is highly overall correlated with DEM and 1 other fieldsHigh correlation
Latitude is highly overall correlated with MAT and 2 other fieldsHigh correlation
Longitude is highly overall correlated with DEM and 6 other fieldsHigh correlation
MAP is highly overall correlated with NDVI and 1 other fieldsHigh correlation
MAT is highly overall correlated with DEM and 6 other fieldsHigh correlation
NDVI is highly overall correlated with MAP and 1 other fieldsHigh correlation
NLCD is highly overall correlated with DEM and 3 other fieldsHigh correlation
SOC is highly overall correlated with MAP and 1 other fieldsHigh correlation
STATE is highly overall correlated with DEM and 6 other fieldsHigh correlation
STATE_ID is highly overall correlated with DEM and 6 other fieldsHigh correlation
SiltClay is highly overall correlated with KFactorHigh correlation
Slope is highly overall correlated with DEM and 3 other fieldsHigh correlation
ID is uniformly distributed Uniform
ID has unique values Unique
Longitude has unique values Unique
Latitude has unique values Unique

Reproduction

Analysis started2025-09-15 01:09:15.225763
Analysis finished2025-09-15 01:09:31.334794
Duration16.11 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

ID
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct471
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean237.71762
Minimum1
Maximum473
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-09-14T21:09:31.411456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile24.5
Q1120.5
median238
Q3355.5
95-th percentile449.5
Maximum473
Range472
Interquartile range (IQR)235

Descriptive statistics

Standard deviation136.53087
Coefficient of variation (CV)0.57434057
Kurtosis-1.1940979
Mean237.71762
Median Absolute Deviation (MAD)118
Skewness-0.0076763503
Sum111965
Variance18640.68
MonotonicityStrictly increasing
2025-09-14T21:09:31.515855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
473 1
 
0.2%
1 1
 
0.2%
2 1
 
0.2%
3 1
 
0.2%
4 1
 
0.2%
5 1
 
0.2%
6 1
 
0.2%
7 1
 
0.2%
8 1
 
0.2%
9 1
 
0.2%
Other values (461) 461
97.9%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
473 1
0.2%
472 1
0.2%
471 1
0.2%
470 1
0.2%
469 1
0.2%
468 1
0.2%
467 1
0.2%
466 1
0.2%
465 1
0.2%
464 1
0.2%

FIPS
Real number (ℝ)

High correlation 

Distinct172
Distinct (%)36.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29232.59
Minimum8001
Maximum56045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-09-14T21:09:31.833738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8001
5-th percentile8033
Q18109
median20195
Q356001
95-th percentile56035
Maximum56045
Range48044
Interquartile range (IQR)47892

Descriptive statistics

Standard deviation18424.533
Coefficient of variation (CV)0.63027371
Kurtosis-1.336933
Mean29232.59
Median Absolute Deviation (MAD)12188
Skewness0.32730239
Sum13768550
Variance3.3946342 × 108
MonotonicityNot monotonic
2025-09-14T21:09:31.937920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56013 14
 
3.0%
56007 12
 
2.5%
56035 9
 
1.9%
56029 9
 
1.9%
35003 8
 
1.7%
56009 8
 
1.7%
35053 7
 
1.5%
56039 7
 
1.5%
56005 7
 
1.5%
56019 7
 
1.5%
Other values (162) 383
81.3%
ValueCountFrequency (%)
8001 1
 
0.2%
8003 2
 
0.4%
8005 3
0.6%
8007 1
 
0.2%
8009 5
1.1%
8013 1
 
0.2%
8015 1
 
0.2%
8017 3
0.6%
8021 1
 
0.2%
8023 1
 
0.2%
ValueCountFrequency (%)
56045 4
0.8%
56043 5
1.1%
56041 2
 
0.4%
56039 7
1.5%
56037 4
0.8%
56035 9
1.9%
56033 4
0.8%
56031 2
 
0.4%
56029 9
1.9%
56027 3
 
0.6%

STATE_ID
Categorical

High correlation 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size27.1 KiB
8
136 
56
120 
35
109 
20
106 

Length

Max length2
Median length2
Mean length1.7112527
Min length1

Characters and Unicode

Total characters806
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row56
2nd row56
3rd row56
4th row56
5th row56

Common Values

ValueCountFrequency (%)
8 136
28.9%
56 120
25.5%
35 109
23.1%
20 106
22.5%

Length

2025-09-14T21:09:32.027283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-14T21:09:32.088983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
8 136
28.9%
56 120
25.5%
35 109
23.1%
20 106
22.5%

Most occurring characters

ValueCountFrequency (%)
5 229
28.4%
8 136
16.9%
6 120
14.9%
3 109
13.5%
2 106
13.2%
0 106
13.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 806
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 229
28.4%
8 136
16.9%
6 120
14.9%
3 109
13.5%
2 106
13.2%
0 106
13.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 806
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 229
28.4%
8 136
16.9%
6 120
14.9%
3 109
13.5%
2 106
13.2%
0 106
13.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 806
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 229
28.4%
8 136
16.9%
6 120
14.9%
3 109
13.5%
2 106
13.2%
0 106
13.2%

STATE
Categorical

High correlation 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size29.9 KiB
Colorado
136 
Wyoming
120 
New Mexico
109 
Kansas
106 

Length

Max length10
Median length8
Mean length7.7579618
Min length6

Characters and Unicode

Total characters3654
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWyoming
2nd rowWyoming
3rd rowWyoming
4th rowWyoming
5th rowWyoming

Common Values

ValueCountFrequency (%)
Colorado 136
28.9%
Wyoming 120
25.5%
New Mexico 109
23.1%
Kansas 106
22.5%

Length

2025-09-14T21:09:32.172940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-14T21:09:32.237397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
colorado 136
23.4%
wyoming 120
20.7%
new 109
18.8%
mexico 109
18.8%
kansas 106
18.3%

Most occurring characters

ValueCountFrequency (%)
o 637
17.4%
a 348
 
9.5%
i 229
 
6.3%
n 226
 
6.2%
e 218
 
6.0%
s 212
 
5.8%
r 136
 
3.7%
C 136
 
3.7%
l 136
 
3.7%
d 136
 
3.7%
Other values (11) 1240
33.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 637
17.4%
a 348
 
9.5%
i 229
 
6.3%
n 226
 
6.2%
e 218
 
6.0%
s 212
 
5.8%
r 136
 
3.7%
C 136
 
3.7%
l 136
 
3.7%
d 136
 
3.7%
Other values (11) 1240
33.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 637
17.4%
a 348
 
9.5%
i 229
 
6.3%
n 226
 
6.2%
e 218
 
6.0%
s 212
 
5.8%
r 136
 
3.7%
C 136
 
3.7%
l 136
 
3.7%
d 136
 
3.7%
Other values (11) 1240
33.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 637
17.4%
a 348
 
9.5%
i 229
 
6.3%
n 226
 
6.2%
e 218
 
6.0%
s 212
 
5.8%
r 136
 
3.7%
C 136
 
3.7%
l 136
 
3.7%
d 136
 
3.7%
Other values (11) 1240
33.9%

COUNTY
Text

Distinct161
Distinct (%)34.2%
Missing0
Missing (%)0.0%
Memory size32.6 KiB
2025-09-14T21:09:32.448663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length17
Mean length13.641189
Min length10

Characters and Unicode

Total characters6425
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)11.3%

Sample

1st rowUinta County
2nd rowLincoln County
3rd rowTeton County
4th rowTeton County
5th rowPark County
ValueCountFrequency (%)
county 471
48.0%
fremont 14
 
1.4%
carbon 12
 
1.2%
park 12
 
1.2%
rio 12
 
1.2%
sublette 9
 
0.9%
converse 8
 
0.8%
baca 8
 
0.8%
san 8
 
0.8%
catron 8
 
0.8%
Other values (160) 420
42.8%
2025-09-14T21:09:32.747565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 767
11.9%
n 731
11.4%
t 620
9.6%
u 549
8.5%
C 545
8.5%
511
 
8.0%
y 508
 
7.9%
a 357
 
5.6%
e 290
 
4.5%
r 226
 
3.5%
Other values (39) 1321
20.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6425
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 767
11.9%
n 731
11.4%
t 620
9.6%
u 549
8.5%
C 545
8.5%
511
 
8.0%
y 508
 
7.9%
a 357
 
5.6%
e 290
 
4.5%
r 226
 
3.5%
Other values (39) 1321
20.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6425
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 767
11.9%
n 731
11.4%
t 620
9.6%
u 549
8.5%
C 545
8.5%
511
 
8.0%
y 508
 
7.9%
a 357
 
5.6%
e 290
 
4.5%
r 226
 
3.5%
Other values (39) 1321
20.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6425
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 767
11.9%
n 731
11.4%
t 620
9.6%
u 549
8.5%
C 545
8.5%
511
 
8.0%
y 508
 
7.9%
a 357
 
5.6%
e 290
 
4.5%
r 226
 
3.5%
Other values (39) 1321
20.6%

Longitude
Real number (ℝ)

High correlation  Unique 

Distinct471
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-104.48656
Minimum-111.01186
Maximum-94.915421
Zeros0
Zeros (%)0.0%
Negative471
Negative (%)100.0%
Memory size3.8 KiB
2025-09-14T21:09:32.847886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-111.01186
5-th percentile-109.59659
Q1-107.53871
median-105.30963
Q3-102.64679
95-th percentile-96.207183
Maximum-94.915421
Range16.096439
Interquartile range (IQR)4.8919236

Descriptive statistics

Standard deviation3.9865906
Coefficient of variation (CV)-0.0381541
Kurtosis-0.29050033
Mean-104.48656
Median Absolute Deviation (MAD)2.36033
Skewness0.75206204
Sum-49213.169
Variance15.892904
MonotonicityStrictly increasing
2025-09-14T21:09:32.950901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-94.91542121 1
 
0.2%
-111.01186 1
 
0.2%
-110.982973 1
 
0.2%
-110.80649 1
 
0.2%
-110.7344173 1
 
0.2%
-110.7307901 1
 
0.2%
-110.66185 1
 
0.2%
-110.64348 1
 
0.2%
-110.595819 1
 
0.2%
-110.5769801 1
 
0.2%
Other values (461) 461
97.9%
ValueCountFrequency (%)
-111.01186 1
0.2%
-110.982973 1
0.2%
-110.80649 1
0.2%
-110.7344173 1
0.2%
-110.7307901 1
0.2%
-110.66185 1
0.2%
-110.64348 1
0.2%
-110.595819 1
0.2%
-110.5769801 1
0.2%
-110.51702 1
0.2%
ValueCountFrequency (%)
-94.91542121 1
0.2%
-95.01079355 1
0.2%
-95.18900004 1
0.2%
-95.23890906 1
0.2%
-95.3651564 1
0.2%
-95.36933051 1
0.2%
-95.37215319 1
0.2%
-95.47712358 1
0.2%
-95.48475339 1
0.2%
-95.52098663 1
0.2%

Latitude
Real number (ℝ)

High correlation  Unique 

Distinct471
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.869818
Minimum31.50637
Maximum44.989732
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-09-14T21:09:33.057462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum31.50637
5-th percentile32.951394
Q137.188095
median38.77066
Q341.076855
95-th percentile44.152655
Maximum44.989732
Range13.483362
Interquartile range (IQR)3.88876

Descriptive statistics

Standard deviation3.2329021
Coefficient of variation (CV)0.083172556
Kurtosis-0.54254939
Mean38.869818
Median Absolute Deviation (MAD)1.87287
Skewness-0.11243351
Sum18307.684
Variance10.451656
MonotonicityNot monotonic
2025-09-14T21:09:33.179566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.28449297 1
 
0.2%
41.05630002 1
 
0.2%
42.88349671 1
 
0.2%
44.53497 1
 
0.2%
44.43288644 1
 
0.2%
44.80635003 1
 
0.2%
44.09124 1
 
0.2%
43.51083 1
 
0.2%
44.31814975 1
 
0.2%
43.69342003 1
 
0.2%
Other values (461) 461
97.9%
ValueCountFrequency (%)
31.50637004 1
0.2%
31.80685194 1
0.2%
31.89556046 1
0.2%
31.93259307 1
0.2%
32.17824002 1
0.2%
32.19851001 1
0.2%
32.23849002 1
0.2%
32.27705996 1
0.2%
32.31215996 1
0.2%
32.35368689 1
0.2%
ValueCountFrequency (%)
44.98973175 1
0.2%
44.95772118 1
0.2%
44.92136235 1
0.2%
44.91846863 1
0.2%
44.84582998 1
0.2%
44.83989999 1
0.2%
44.80635003 1
0.2%
44.77946004 1
0.2%
44.7719228 1
0.2%
44.75683001 1
0.2%

SOC
Real number (ℝ)

High correlation 

Distinct456
Distinct (%)97.6%
Missing4
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean6.3507623
Minimum0.408
Maximum30.473
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-09-14T21:09:33.278895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.408
5-th percentile0.9637
Q12.7695
median4.971
Q38.7135
95-th percentile16.5219
Maximum30.473
Range30.065
Interquartile range (IQR)5.944

Descriptive statistics

Standard deviation5.0454091
Coefficient of variation (CV)0.79445725
Kurtosis2.4271923
Mean6.3507623
Median Absolute Deviation (MAD)2.619
Skewness1.4647284
Sum2965.806
Variance25.456153
MonotonicityNot monotonic
2025-09-14T21:09:33.381906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.02 2
 
0.4%
2.884 2
 
0.4%
1.015 2
 
0.4%
3.595 2
 
0.4%
4.594 2
 
0.4%
11.22 2
 
0.4%
1.35 2
 
0.4%
10.076 2
 
0.4%
4.83 2
 
0.4%
4.974 2
 
0.4%
Other values (446) 447
94.9%
(Missing) 4
 
0.8%
ValueCountFrequency (%)
0.408 1
0.2%
0.446 1
0.2%
0.462 1
0.2%
0.471 1
0.2%
0.485 1
0.2%
0.494 1
0.2%
0.5 1
0.2%
0.528 1
0.2%
0.605 1
0.2%
0.606 1
0.2%
ValueCountFrequency (%)
30.473 1
0.2%
27.984 1
0.2%
24.954 1
0.2%
23.19 1
0.2%
23.058 1
0.2%
21.644 1
0.2%
21.591 1
0.2%
21.455 1
0.2%
21.125 1
0.2%
21.076 1
0.2%

DEM
Real number (ℝ)

High correlation 

Distinct464
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1631.1063
Minimum258.6488
Maximum3618.0242
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-09-14T21:09:33.482686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum258.6488
5-th percentile353.76965
Q11175.3314
median1592.8932
Q32234.2649
95-th percentile2797.0868
Maximum3618.0242
Range3359.3754
Interquartile range (IQR)1058.9335

Descriptive statistics

Standard deviation767.69233
Coefficient of variation (CV)0.47065744
Kurtosis-0.80391609
Mean1631.1063
Median Absolute Deviation (MAD)590.00305
Skewness-0.02350235
Sum768251.07
Variance589351.51
MonotonicityNot monotonic
2025-09-14T21:09:33.583048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1306.727661 2
 
0.4%
1561.74939 2
 
0.4%
2439.060547 2
 
0.4%
313.8303223 2
 
0.4%
1986.571533 2
 
0.4%
2465.521973 2
 
0.4%
2328.449463 2
 
0.4%
1298.654541 1
 
0.2%
1331.444092 1
 
0.2%
1063.505127 1
 
0.2%
Other values (454) 454
96.4%
ValueCountFrequency (%)
258.6488037 1
0.2%
269.3353577 1
0.2%
272.2623901 1
0.2%
272.6796875 1
0.2%
277.6633606 1
0.2%
293.8309326 1
0.2%
296.3933716 1
0.2%
298.0536804 1
0.2%
307.7427063 1
0.2%
313.1699219 1
0.2%
ValueCountFrequency (%)
3618.02417 1
0.2%
3471.049316 1
0.2%
3376.358154 1
0.2%
3376.141846 1
0.2%
3232.158203 1
0.2%
3124.866211 1
0.2%
3123.391602 1
0.2%
3111.41333 1
0.2%
3109.554443 1
0.2%
3095.490479 1
0.2%

Aspect
Real number (ℝ)

Distinct464
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165.46766
Minimum86.894569
Maximum255.83353
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-09-14T21:09:33.684171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum86.894569
5-th percentile130.00969
Q1148.80523
median164.07072
Q3179.08429
95-th percentile208.19434
Maximum255.83353
Range168.93896
Interquartile range (IQR)30.27906

Descriptive statistics

Standard deviation24.337033
Coefficient of variation (CV)0.1470803
Kurtosis0.96876512
Mean165.46766
Median Absolute Deviation (MAD)15.263336
Skewness0.53432801
Sum77935.267
Variance592.29118
MonotonicityNot monotonic
2025-09-14T21:09:33.787373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
190.9215546 2
 
0.4%
138.082428 2
 
0.4%
153.8435516 2
 
0.4%
176.8919983 2
 
0.4%
175.594574 2
 
0.4%
136.4089966 2
 
0.4%
219.6880188 2
 
0.4%
208.2095642 1
 
0.2%
170.2262115 1
 
0.2%
164.7299347 1
 
0.2%
Other values (454) 454
96.4%
ValueCountFrequency (%)
86.8945694 1
0.2%
106.511261 1
0.2%
110.139595 1
0.2%
110.2962875 1
0.2%
115.2586517 1
0.2%
117.2919388 1
0.2%
118.9809952 1
0.2%
120.0071259 1
0.2%
120.4855576 1
0.2%
121.1834183 1
0.2%
ValueCountFrequency (%)
255.8335266 1
0.2%
254.6745758 1
0.2%
250.3361969 1
0.2%
250.1024017 1
0.2%
234.4831238 1
0.2%
232.394577 1
0.2%
229.5141144 1
0.2%
224.0729828 1
0.2%
223.6387939 1
0.2%
223.1869812 1
0.2%

Slope
Real number (ℝ)

High correlation 

Distinct464
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8267399
Minimum0.64925271
Maximum26.104162
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-09-14T21:09:33.892754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.64925271
5-th percentile0.93206206
Q11.4506671
median2.7266774
Q37.1070788
95-th percentile14.707602
Maximum26.104162
Range25.45491
Interquartile range (IQR)5.6564116

Descriptive statistics

Standard deviation4.6888761
Coefficient of variation (CV)0.9714375
Kurtosis2.4410859
Mean4.8267399
Median Absolute Deviation (MAD)1.5953804
Skewness1.6325236
Sum2273.3945
Variance21.985559
MonotonicityNot monotonic
2025-09-14T21:09:33.992424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.493518829 2
 
0.4%
4.469407558 2
 
0.4%
3.570212603 2
 
0.4%
2.361840725 2
 
0.4%
4.573308945 2
 
0.4%
10.10852718 2
 
0.4%
10.4627409 2
 
0.4%
1.716193557 1
 
0.2%
1.872875452 1
 
0.2%
1.025621295 1
 
0.2%
Other values (454) 454
96.4%
ValueCountFrequency (%)
0.649252713 1
0.2%
0.753047287 1
0.2%
0.781253636 1
0.2%
0.785101414 1
0.2%
0.789641201 1
0.2%
0.800130606 1
0.2%
0.805040777 1
0.2%
0.817521036 1
0.2%
0.835930109 1
0.2%
0.858109653 1
0.2%
ValueCountFrequency (%)
26.10416222 1
0.2%
24.94082069 1
0.2%
20.80967331 1
0.2%
20.4222908 1
0.2%
20.31998825 1
0.2%
19.7778759 1
0.2%
19.34244156 1
0.2%
19.16675568 1
0.2%
18.98168182 1
0.2%
18.61675262 1
0.2%

TPI
Real number (ℝ)

Distinct464
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.00066909909
Minimum-26.708651
Maximum16.706257
Zeros0
Zeros (%)0.0%
Negative241
Negative (%)51.2%
Memory size3.8 KiB
2025-09-14T21:09:34.087976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-26.708651
5-th percentile-5.2362919
Q1-0.81605425
median-0.04758827
Q30.8490718
95-th percentile6.231636
Maximum16.706257
Range43.414907
Interquartile range (IQR)1.6651261

Descriptive statistics

Standard deviation3.5673903
Coefficient of variation (CV)-5331.6323
Kurtosis10.718063
Mean-0.00066909909
Median Absolute Deviation (MAD)0.83830066
Skewness-1.0871291
Sum-0.31514567
Variance12.726274
MonotonicityNot monotonic
2025-09-14T21:09:34.188879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.457343131 2
 
0.4%
-2.700242758 2
 
0.4%
-2.728082418 2
 
0.4%
-0.885888934 2
 
0.4%
1.805906773 2
 
0.4%
8.48152256 2
 
0.4%
0.551476836 2
 
0.4%
-0.02701824 1
 
0.2%
-0.133461028 1
 
0.2%
0.251985759 1
 
0.2%
Other values (454) 454
96.4%
ValueCountFrequency (%)
-26.70865059 1
0.2%
-16.92754936 1
0.2%
-14.92387867 1
0.2%
-13.91132736 1
0.2%
-12.696311 1
0.2%
-11.65337181 1
0.2%
-11.63264465 1
0.2%
-10.73035431 1
0.2%
-9.535725594 1
0.2%
-9.479537964 1
0.2%
ValueCountFrequency (%)
16.70625687 1
0.2%
11.70857334 1
0.2%
10.79698753 1
0.2%
10.26024818 1
0.2%
10.1651659 1
0.2%
9.453812599 1
0.2%
9.318076134 1
0.2%
9.142570496 1
0.2%
9.017038345 1
0.2%
8.48152256 2
0.4%

KFactor
Real number (ℝ)

High correlation 

Distinct386
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.25589651
Minimum0.050000001
Maximum0.43000001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-09-14T21:09:34.285836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.050000001
5-th percentile0.10331515
Q10.19333572
median0.28
Q30.31999999
95-th percentile0.37000001
Maximum0.43000001
Range0.38000001
Interquartile range (IQR)0.12666427

Descriptive statistics

Standard deviation0.08560191
Coefficient of variation (CV)0.3345177
Kurtosis-0.64396548
Mean0.25589651
Median Absolute Deviation (MAD)0.053636372
Skewness-0.54135158
Sum120.52726
Variance0.0073276871
MonotonicityNot monotonic
2025-09-14T21:09:34.390963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.319999993 53
 
11.3%
0.370000005 10
 
2.1%
0.200000003 7
 
1.5%
0.280000001 6
 
1.3%
0.170000002 4
 
0.8%
0.100000001 2
 
0.4%
0.153263152 2
 
0.4%
0.149090916 2
 
0.4%
0.12565656 2
 
0.4%
0.260937512 2
 
0.4%
Other values (376) 381
80.9%
ValueCountFrequency (%)
0.050000001 2
0.4%
0.05102041 1
0.2%
0.053296704 1
0.2%
0.05468085 1
0.2%
0.055 1
0.2%
0.058526315 1
0.2%
0.059 1
0.2%
0.059500001 1
0.2%
0.05979592 1
0.2%
0.064130433 1
0.2%
ValueCountFrequency (%)
0.430000007 1
0.2%
0.412197798 1
0.2%
0.411818177 1
0.2%
0.410430104 1
0.2%
0.403950632 1
0.2%
0.392098755 1
0.2%
0.388297886 1
0.2%
0.38595745 1
0.2%
0.384375006 1
0.2%
0.377499998 1
0.2%

MAP
Real number (ℝ)

High correlation 

Distinct464
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean499.37295
Minimum193.91322
Maximum1128.1145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-09-14T21:09:34.492858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum193.91322
5-th percentile261.50912
Q1352.77451
median432.6304
Q3590.42691
95-th percentile927.77014
Maximum1128.1145
Range934.20128
Interquartile range (IQR)237.6524

Descriptive statistics

Standard deviation206.93592
Coefficient of variation (CV)0.41439153
Kurtosis0.46982257
Mean499.37295
Median Absolute Deviation (MAD)102.67889
Skewness1.0825393
Sum235204.66
Variance42822.475
MonotonicityNot monotonic
2025-09-14T21:09:34.595988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
425.2972107 2
 
0.4%
369.6527405 2
 
0.4%
244.2666168 2
 
0.4%
927.7701416 2
 
0.4%
261.5091248 2
 
0.4%
489.2418823 2
 
0.4%
508.8981934 2
 
0.4%
393.8974915 1
 
0.2%
401.3128357 1
 
0.2%
342.2649231 1
 
0.2%
Other values (454) 454
96.4%
ValueCountFrequency (%)
193.9132233 1
0.2%
194.2818298 1
0.2%
195.2479248 1
0.2%
201.5090637 1
0.2%
204.9618225 1
0.2%
205.0567932 1
0.2%
206.8027039 1
0.2%
207.0542603 1
0.2%
210.6248474 1
0.2%
229.827774 1
0.2%
ValueCountFrequency (%)
1128.114502 1
0.2%
1126.816528 1
0.2%
1121.274414 1
0.2%
1115.609253 1
0.2%
1109.410767 1
0.2%
1099.34082 1
0.2%
1098.397339 1
0.2%
1080.718384 1
0.2%
1060.175659 1
0.2%
1043.991333 1
0.2%

MAT
Real number (ℝ)

High correlation 

Distinct463
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.8855211
Minimum-0.5910638
Maximum16.874287
Zeros0
Zeros (%)0.0%
Negative6
Negative (%)1.3%
Memory size3.8 KiB
2025-09-14T21:09:34.694221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.5910638
5-th percentile1.6258606
Q15.8800533
median9.1728353
Q312.444286
95-th percentile14.638992
Maximum16.874287
Range17.46535
Interquartile range (IQR)6.5642326

Descriptive statistics

Standard deviation4.0981336
Coefficient of variation (CV)0.46121478
Kurtosis-0.82365673
Mean8.8855211
Median Absolute Deviation (MAD)3.2924228
Skewness-0.27522458
Sum4185.0804
Variance16.794699
MonotonicityNot monotonic
2025-09-14T21:09:34.797066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.056969643 2
 
0.4%
3.173333406 2
 
0.4%
5.51282835 2
 
0.4%
2.535979271 2
 
0.4%
11.69946766 2
 
0.4%
9.521315575 2
 
0.4%
7.075252533 2
 
0.4%
6.232395649 2
 
0.4%
14.73075008 1
 
0.2%
14.26611137 1
 
0.2%
Other values (453) 453
96.2%
ValueCountFrequency (%)
-0.591063797 1
0.2%
-0.342474163 1
0.2%
-0.309166729 1
0.2%
-0.213022023 1
0.2%
-0.143489569 1
0.2%
-0.103999995 1
0.2%
0.382888913 1
0.2%
0.470781118 1
0.2%
0.702472448 1
0.2%
0.758826554 1
0.2%
ValueCountFrequency (%)
16.87428665 1
0.2%
16.74279022 1
0.2%
16.67824936 1
0.2%
16.67785645 1
0.2%
16.30372429 1
0.2%
16.19720078 1
0.2%
15.98961067 1
0.2%
15.97299957 1
0.2%
15.94797993 1
0.2%
15.7054081 1
0.2%

NDVI
Real number (ℝ)

High correlation 

Distinct464
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.43543111
Minimum0.14243349
Maximum0.79699218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-09-14T21:09:34.898956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.14243349
5-th percentile0.19207376
Q10.30534685
median0.41568252
Q30.55590254
95-th percentile0.72162244
Maximum0.79699218
Range0.65455869
Interquartile range (IQR)0.25055569

Descriptive statistics

Standard deviation0.16202394
Coefficient of variation (CV)0.37210004
Kurtosis-0.91804181
Mean0.43543111
Median Absolute Deviation (MAD)0.13058659
Skewness0.23375088
Sum205.08805
Variance0.026251756
MonotonicityNot monotonic
2025-09-14T21:09:35.215119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.395361334 2
 
0.4%
0.376411051 2
 
0.4%
0.259133458 2
 
0.4%
0.796992183 2
 
0.4%
0.218770355 2
 
0.4%
0.531961083 2
 
0.4%
0.573623061 2
 
0.4%
0.275360823 1
 
0.2%
0.285669565 1
 
0.2%
0.270067871 1
 
0.2%
Other values (454) 454
96.4%
ValueCountFrequency (%)
0.142433494 1
0.2%
0.148967952 1
0.2%
0.153097853 1
0.2%
0.162046626 1
0.2%
0.162069917 1
0.2%
0.163638309 1
0.2%
0.164828882 1
0.2%
0.165527865 1
0.2%
0.167451352 1
0.2%
0.169441774 1
0.2%
ValueCountFrequency (%)
0.796992183 2
0.4%
0.781474531 1
0.2%
0.773537636 1
0.2%
0.770299137 1
0.2%
0.755763054 1
0.2%
0.750859082 1
0.2%
0.750725389 1
0.2%
0.749037266 1
0.2%
0.746697128 1
0.2%
0.737809896 1
0.2%

SiltClay
Real number (ℝ)

High correlation 

Distinct462
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.677916
Minimum9.1619568
Maximum89.834412
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-09-14T21:09:35.314832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9.1619568
5-th percentile26.452091
Q142.75873
median52.112766
Q362.850862
95-th percentile82.898888
Maximum89.834412
Range80.672455
Interquartile range (IQR)20.092133

Descriptive statistics

Standard deviation17.239242
Coefficient of variation (CV)0.3211608
Kurtosis-0.32169567
Mean53.677916
Median Absolute Deviation (MAD)9.8622627
Skewness0.11049198
Sum25282.298
Variance297.19148
MonotonicityNot monotonic
2025-09-14T21:09:35.419906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.69052696 2
 
0.4%
58.14123535 2
 
0.4%
33.08124924 2
 
0.4%
53.33737564 2
 
0.4%
88.55212402 2
 
0.4%
31.43030357 2
 
0.4%
42 2
 
0.4%
54.03131485 2
 
0.4%
31.89999962 2
 
0.4%
46.90714264 1
 
0.2%
Other values (452) 452
96.0%
ValueCountFrequency (%)
9.161956787 1
0.2%
9.253535271 1
0.2%
10.70105267 1
0.2%
12.74591827 1
0.2%
13.47474766 1
0.2%
13.82199955 1
0.2%
14.82857132 1
0.2%
14.99354839 1
0.2%
17.39800072 1
0.2%
17.69052696 2
0.4%
ValueCountFrequency (%)
89.83441162 1
0.2%
88.55212402 2
0.4%
88.5 1
0.2%
88.42424011 1
0.2%
88.42021179 1
0.2%
87.80515289 1
0.2%
87.71546173 1
0.2%
87.42795563 1
0.2%
87.1631546 1
0.2%
87.06046295 1
0.2%

NLCD
Categorical

High correlation 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
Herbaceous
151 
Shrubland
130 
Planted/Cultivated
97 
Forest
93 

Length

Max length18
Median length10
Mean length10.581741
Min length6

Characters and Unicode

Total characters4984
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowShrubland
2nd rowShrubland
3rd rowForest
4th rowForest
5th rowForest

Common Values

ValueCountFrequency (%)
Herbaceous 151
32.1%
Shrubland 130
27.6%
Planted/Cultivated 97
20.6%
Forest 93
19.7%

Length

2025-09-14T21:09:35.516337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-14T21:09:35.575529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
herbaceous 151
32.1%
shrubland 130
27.6%
planted/cultivated 97
20.6%
forest 93
19.7%

Most occurring characters

ValueCountFrequency (%)
e 589
11.8%
a 475
 
9.5%
t 384
 
7.7%
u 378
 
7.6%
r 374
 
7.5%
d 324
 
6.5%
l 324
 
6.5%
b 281
 
5.6%
s 244
 
4.9%
o 244
 
4.9%
Other values (11) 1367
27.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4984
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 589
11.8%
a 475
 
9.5%
t 384
 
7.7%
u 378
 
7.6%
r 374
 
7.5%
d 324
 
6.5%
l 324
 
6.5%
b 281
 
5.6%
s 244
 
4.9%
o 244
 
4.9%
Other values (11) 1367
27.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4984
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 589
11.8%
a 475
 
9.5%
t 384
 
7.7%
u 378
 
7.6%
r 374
 
7.5%
d 324
 
6.5%
l 324
 
6.5%
b 281
 
5.6%
s 244
 
4.9%
o 244
 
4.9%
Other values (11) 1367
27.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4984
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 589
11.8%
a 475
 
9.5%
t 384
 
7.7%
u 378
 
7.6%
r 374
 
7.5%
d 324
 
6.5%
l 324
 
6.5%
b 281
 
5.6%
s 244
 
4.9%
o 244
 
4.9%
Other values (11) 1367
27.4%

FRG
Categorical

High correlation 

Distinct6
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size35.6 KiB
Fire Regime Group II
252 
Fire Regime Group III
100 
Fire Regime Group IV
75 
Fire Regime Group I
 
19
Fire Regime Group V
 
18

Length

Max length21
Median length20
Mean length20.089172
Min length17

Characters and Unicode

Total characters9462
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFire Regime Group IV
2nd rowFire Regime Group IV
3rd rowFire Regime Group V
4th rowFire Regime Group V
5th rowFire Regime Group V

Common Values

ValueCountFrequency (%)
Fire Regime Group II 252
53.5%
Fire Regime Group III 100
 
21.2%
Fire Regime Group IV 75
 
15.9%
Fire Regime Group I 19
 
4.0%
Fire Regime Group V 18
 
3.8%
Indeterminate FRG 7
 
1.5%

Length

2025-09-14T21:09:35.658672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-14T21:09:35.728658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fire 464
24.8%
regime 464
24.8%
group 464
24.8%
ii 252
13.5%
iii 100
 
5.3%
iv 75
 
4.0%
i 19
 
1.0%
v 18
 
1.0%
indeterminate 7
 
0.4%
frg 7
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e 1413
14.9%
1399
14.8%
r 935
9.9%
i 935
9.9%
I 905
9.6%
F 471
 
5.0%
m 471
 
5.0%
G 471
 
5.0%
R 471
 
5.0%
g 464
 
4.9%
Other values (8) 1527
16.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9462
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1413
14.9%
1399
14.8%
r 935
9.9%
i 935
9.9%
I 905
9.6%
F 471
 
5.0%
m 471
 
5.0%
G 471
 
5.0%
R 471
 
5.0%
g 464
 
4.9%
Other values (8) 1527
16.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9462
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1413
14.9%
1399
14.8%
r 935
9.9%
i 935
9.9%
I 905
9.6%
F 471
 
5.0%
m 471
 
5.0%
G 471
 
5.0%
R 471
 
5.0%
g 464
 
4.9%
Other values (8) 1527
16.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9462
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1413
14.9%
1399
14.8%
r 935
9.9%
i 935
9.9%
I 905
9.6%
F 471
 
5.0%
m 471
 
5.0%
G 471
 
5.0%
R 471
 
5.0%
g 464
 
4.9%
Other values (8) 1527
16.1%

Interactions

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2025-09-14T21:09:21.299802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:09:22.342272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:09:23.583454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:09:24.562060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:09:25.486379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:09:26.540610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:09:27.736896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:09:28.782847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:09:29.815453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:09:30.989232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:09:17.017818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:09:18.005167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:09:19.314319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:09:20.345198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:09:21.375832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:09:22.417935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:09:23.655894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:09:24.633072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:09:25.556216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:09:26.619642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:09:27.805973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:09:28.858339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-14T21:09:29.889182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-09-14T21:09:35.822554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AspectDEMFIPSFRGIDKFactorLatitudeLongitudeMAPMATNDVINLCDSOCSTATESTATE_IDSiltClaySlopeTPI
Aspect1.0000.2270.0200.226-0.252-0.085-0.003-0.2520.104-0.1940.1160.2480.0860.1380.138-0.0690.3150.054
DEM0.2271.0000.0230.410-0.793-0.5680.107-0.793-0.205-0.812-0.0290.5780.0900.5740.574-0.4410.734-0.004
FIPS0.0200.0231.0000.421-0.3570.1510.298-0.357-0.316-0.132-0.3430.458-0.1091.0001.000-0.1490.0960.001
FRG0.2260.4100.4211.0000.4280.2430.3020.4090.2650.4120.2410.5190.1230.4210.4210.2080.3610.250
ID-0.252-0.793-0.3570.4281.0000.382-0.1701.0000.4320.6380.2990.5330.0790.6000.6000.383-0.6630.020
KFactor-0.085-0.5680.1510.2430.3821.0000.1140.3820.0600.345-0.0440.3020.0440.3580.3580.595-0.401-0.026
Latitude-0.0030.1070.2980.302-0.1700.1141.000-0.170-0.014-0.6080.1960.3530.2820.8130.813-0.0390.191-0.034
Longitude-0.252-0.793-0.3570.4091.0000.382-0.1701.0000.4320.6380.2990.5370.0790.6200.6200.383-0.6630.020
MAP0.104-0.205-0.3160.2650.4320.060-0.0140.4321.0000.0680.8530.4090.5740.4150.4150.4360.0630.171
MAT-0.194-0.812-0.1320.4120.6380.345-0.6080.6380.0681.000-0.1960.470-0.2930.5980.5980.289-0.6540.009
NDVI0.116-0.029-0.3430.2410.299-0.0440.1960.2990.853-0.1961.0000.4670.6480.3840.3840.3020.1810.129
NLCD0.2480.5780.4580.5190.5330.3020.3530.5370.4090.4700.4671.0000.2640.4580.4580.3780.4930.319
SOC0.0860.090-0.1090.1230.0790.0440.2820.0790.574-0.2930.6480.2641.0000.2300.2300.2890.2910.059
STATE0.1380.5741.0000.4210.6000.3580.8130.6200.4150.5980.3840.4580.2301.0001.0000.4830.3120.179
STATE_ID0.1380.5741.0000.4210.6000.3580.8130.6200.4150.5980.3840.4580.2301.0001.0000.4830.3120.179
SiltClay-0.069-0.441-0.1490.2080.3830.595-0.0390.3830.4360.2890.3020.3780.2890.4830.4831.000-0.173-0.025
Slope0.3150.7340.0960.361-0.663-0.4010.191-0.6630.063-0.6540.1810.4930.2910.3120.312-0.1731.0000.034
TPI0.054-0.0040.0010.2500.020-0.026-0.0340.0200.1710.0090.1290.3190.0590.1790.179-0.0250.0341.000

Missing values

2025-09-14T21:09:31.129165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-14T21:09:31.257157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDFIPSSTATE_IDSTATECOUNTYLongitudeLatitudeSOCDEMAspectSlopeTPIKFactorMAPMATNDVISiltClayNLCDFRG
015604156WyomingUinta County-111.01186041.05630015.7632229.078613159.1877445.671615-0.0857240.320000468.3244934.5951690.41393964.842697ShrublandFire Regime Group IV
125602356WyomingLincoln County-110.98297342.88349715.8831889.400146156.8785558.9138124.5591320.261212536.3521733.8599240.69395372.004547ShrublandFire Regime Group IV
235603956WyomingTeton County-110.80649044.53497018.1422423.048340168.6123504.7748052.6058870.216200859.5509030.8855000.54660357.187000ForestFire Regime Group V
345603956WyomingTeton County-110.73441744.43288610.7452484.282715198.3536227.1218115.1469310.181667869.4724120.4707810.61910154.991665ForestFire Regime Group V
455602956WyomingPark County-110.73079044.80635010.4792396.194580201.3214877.9498643.7557060.125510802.9743040.7588270.58447251.228573ForestFire Regime Group V
565603956WyomingTeton County-110.66185044.09124016.9872360.572998208.9731609.6632156.5021560.2682221121.2744141.3586670.60283545.020000ForestFire Regime Group V
675603956WyomingTeton County-110.64348043.51083024.9542254.791260254.6745768.940430-9.3831870.184316610.8505862.8187890.52256150.295788ForestFire Regime Group IV
785603956WyomingTeton County-110.59581944.3181506.2882496.393555189.52731312.5829506.3335410.2038891109.4107670.3828890.46377352.413334ShrublandFire Regime Group V
895603956WyomingTeton County-110.57698043.69342021.4552238.686768234.4831249.6814111.0905850.319785817.9403082.1004840.65254149.159142ForestFire Regime Group IV
9105603556WyomingSublette County-110.51702043.17939021.6442228.609863152.17927515.030870-6.7860530.058526561.0355221.9427370.69170344.122105ForestFire Regime Group IV
IDFIPSSTATE_IDSTATECOUNTYLongitudeLatitudeSOCDEMAspectSlopeTPIKFactorMAPMATNDVISiltClayNLCDFRG
4614642000520KansasAtchison County-95.52098739.56081610.728323.997589180.0093081.8581060.3301250.311263937.00988812.2180530.72608374.167366Planted/CultivatedFire Regime Group II
4624652008720KansasJefferson County-95.48475339.13829712.903296.393372184.4024053.3716653.3688280.368571954.42529312.4502380.75085979.306351Planted/CultivatedFire Regime Group II
4634662009920KansasLabette County-95.47712437.2619827.256272.679688185.0403901.4299681.0744590.3255291099.34082013.7481170.69431477.164703Planted/CultivatedFire Regime Group I
4644672001320KansasBrown County-95.37215339.8823297.500313.830322176.8919982.361841-0.8858890.280000927.77014211.6994680.79699288.552124Planted/CultivatedFire Regime Group II
4654682009920KansasLabette County-95.36933137.12324013.147269.335358177.4589231.011653-0.1606410.3859571115.60925313.9399470.67338681.658508Planted/CultivatedFire Regime Group II
4664692001320KansasBrown County-95.36515639.8479886.433313.830322176.8919982.361841-0.8858890.280000927.77014211.6994680.79699288.552124Planted/CultivatedFire Regime Group II
4674702013320KansasNeosho County-95.23890937.6914218.780293.830933184.4200741.3907490.6099010.3587911098.39733913.6428020.72152683.907692Planted/CultivatedFire Regime Group II
4684712000120KansasAllen County-95.18900037.93123010.672323.660980147.1656801.0671600.5195720.3422111080.71838413.4258950.69066674.418945Planted/CultivatedFire Regime Group II
4694722004320KansasDoniphan County-95.01079439.8113064.488298.053680173.3465123.1455341.1193820.287957935.52948011.8026340.77353889.834412Planted/CultivatedFire Regime Group II
4704732002120KansasCherokee County-94.91542137.2844936.975272.262390194.9636840.917755-0.2569610.1926031126.81652813.8499320.66175879.964386Planted/CultivatedFire Regime Group V